Graph Autoencoders for Business Process Anomaly Detection
siyu huo, Hagen Völzer, et al.
BPM 2021
Cash flow prediction of a bank is an important task as it is not only related to liquidity risk but is also regulated by financial authorities. To improve the prediction, a graph analysis of bank transaction data is promising, while its size, scale-free nature, and various attributes make the task challenging.In this paper, we propose a graph-based machine learning method for the cash flow prediction task. Our contributions are as follows. (i) We introduce an extensible and scalable shared-memory parallel graph analysis platform that supports the vertex-centric, bulk synchronous parallel programming paradigm. (ii) We introduce two novel graph features upon the platform: (ii-a) an internal money flow feature based on the Markov process approximation, and (ii-b) an anomaly score feature derived from other graph features.The proposed method is examined with real bank transaction data. The proposed graph features reduce the error of a long-term (31-day) cash flow prediction by 56 % from that of a non-graph-based time-series prediction model. The graph analysis platform can compute graph features from a graph with 10 × 10^6 nodes and 593 × 10^6 edges in 2 hours 20 minutes.
siyu huo, Hagen Völzer, et al.
BPM 2021
Michelle Brachman, Christopher Bygrave, et al.
AAAI 2022
Neil Thompson, Martin Fleming, et al.
IAAI 2024
Vladimir Lipets, Alexander Zadorojniy
MTCSPTA 2021